SWIPT-NOMA-D2D FOR 6G UAV NETWORKS: ENHANCING THROUGHPUT AND ENERGY EFFICIENCY

Abstract

This paper investigates the integration of Simultaneous Wireless Information and Power Transfer (SWIPT) into UAV-assisted 6G networks by employing Non-Orthogonal Multiple Access (NOMA) and Device-to-Device (D2D) communications. The proposed system model aims to optimize throughput and energy efficiency (EE) through effective power allocation, energy harvesting, and interference management. A multi-objective optimization problem is formulated under practical constraints such as UAV mobility and channel uncertainty. To tackle the non-convex nature of the problem, a hybrid algorithm combining Deep Reinforcement Learning (DRL) and convex optimization is developed. Simulation results demonstrate significant improvements in EE and increased throughput compared to conventional OMA-based UAV networks. This study contributes to establishing a critical foundation for future sustainable and high-performance 6G UAV system